Convolutional Neural Networks (CNN) are progressively being combined with imbalanced data sets [1]} and yet they memorize training data due to over-parameterization [2]}. When learning from imbalanced classification data, a neural network's affinity for memorization limits its generalization capacity for minority classes [3]}. Traditionally, a machine learning model's ability to generalize has been measured by the difference between training and testing accuracy rates [4]}. In order to gain more insight into the generalization gap in the context of imbalanced deep learning, in this work we measure the generalization gap as the class-wise difference between a model's internal representation of training and testing features. Specifically, we measure the difference in the ranges of the embedded features, which is illustrated in Figure  REF . This provides a wealth of information on where generalization is failing and how it can be rectified.
